Author:
Zhu Zhichao,Chen Feiyang,Ni Lei,Bian Haitao,Jiang Juncheng,Chen Zhiquan
Funder
National Natural Science Foundation of China
Reference52 articles.
1. A data-driven Bayesian network learning method for process fault diagnosis;Amin;Process Saf. Environ. Prot.,2021
2. An analysis of process fault diagnosis methods from safety perspectives;Arunthavanathan;Comput. Chem. Eng.,2021
3. Bai, S., Kolter, J.Z., Koltun, V., 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.
4. A novel transformer-based multi-variable multi-step prediction method for chemical process fault prognosis;Bai;Process Saf. Environ. Prot.,2023
5. One step forward for smart chemical process fault detection and diagnosis;Bi;Comput. Chem. Eng.,2022